Thriving companies, tech or otherwise, have been aware of data’s crucial role in shaping business decisions for decades. However, it’s become clear recently how little value they’ve actually been getting from the data they collect.
What has changed? The scope of data acquisition and our ability to make sense of it all.
This article highlights the transformative relationship between data lakes & artificial intelligence. It explores how the shift towards all-encompassing storage data lakes makes it possible to empower AI to predict, simplify, and enhance business decisions.
Redefining Data Storage & Usage
Data lakes came about as the solution to modern information storage challenges. On the one hand, even modest businesses generate so much data via sales, marketing efforts, interactions with customers & third-party vendors, etc., that storing and making sense of it is becoming impractical.
On the other, much data businesses do store either isn’t used to its full potential or becomes part of data silos only some departments benefit from. Traditional data storage is limited, segregated, and dependent on conventional formatting restrictions.
Data lakes change all of this. They’re repositories that take in copious amounts of data indiscriminately. The lake analogy refers to their ability to accommodate data from various sources or streams. It doesn’t matter whether the data in question is an unstructured influx of social media posts & images or a meticulously organized database – the lake has room for it all.
More importantly for AI and machine learning, data lakes don’t use a traditional file & folder structure. Everything resides on the same level, while metadata facilitates organization and searches.
Such a hodgepodge approach looks counterintuitive at first. However, it gives researchers, data scientists, executives, and AI algorithms that leverage data lakes more freedom in structuring their queries. Crucially, the disparate yet unified data is the perfect raw material AI can turn into actionable insights.
An Untapped Treasure Trove
The amount and diversity of information data lakes store makes its raw form unsuitable for human use & processing. That’s where artificial intelligence comes in. Here are the most common methods it uses to transform large quantities of chaotic data into intelligence humans can understand and businesses benefit from.
Data Preparation
Simply storing data isn’t a guarantee of accuracy or relevance for analysis. The data prep stage is invaluable for ensuring the quality and compatibility of data further testing and analytics tools will use. This stage used to be time-consuming and prone to human error. Leveraging artificial intelligence from the get-go ensures both human actors and further AI refinement tools have the best possible starting conditions for their research and insight generation.
Pattern recognition
Seeing trends and patterns where humans can’t is among AI’s most sought-after advantages. Access to a vault of past data lets AI use predictive analytics to highlight future trends stakeholders can capitalize on.
For example, analyzing past sales records might uncover an upward trend during a time of year a business hasn’t considered and let it capitalize on it come next year. Likewise, predictive analytics can help curb unnecessary logistics costs. In production environments, it can leverage IoT sensor data and help develop an optimized maintenance schedule that eliminates downtime and ensures timely repairs.
Real-time data presentation & visualization
Data lakes store and make information available from different sources. Similarly, machine learning algorithms can use this information to generate a holistic real-time view of various business metrics. This allows stakeholders with different tech familiarity levels to benefit from a data lake’s stores and implement them into a business’s growth strategies.
Customer profiling & marketing personalization
On the one hand, AI helps companies refine their production processes, products, and long-term goals. On the other, it plays an instrumental role in modern marketing. Advertising burnout is real and growing. It’s not that customers buy less or don’t want exposure to new products. Rather, the vast majority expect a personalized experience, which data-driven marketing is happy to provide.
The trend dovetails with the switch towards prioritizing first-party customer data, i.e., things people share willingly via sign-ups, surveys, etc. Feeding such data into machine learning models and integrating them with existing tools lets marketing departments segment audiences to an unprecedented degree and will eventually lead to truly bespoke campaigns.
Sentiment analysis
Their inherent compatibility with unstructured data makes data lakes ideal sources for a mix of qualitative and quantitative research. Specifically, large language models can use social media posts, reviews, or customer support interactions to gauge the public’s opinions on existing products. The results may also identify common pain points or help test the waters before making sweeping changes to a brand’s identity.
Are There Challenges?
Data lakes’ nature comes with two key considerations.
The first has to do with security. Businesses already store sensitive user data, patents, not-yet-public marketing materials, and other vulnerable information. Stringent access measures and safeguards need to be in effect for any data lake that houses such information, whether it’s cloud-based or on-premise.
Securing access is paramount, especially since less tech-savvy employees now draw on artificial intelligence to enhance their productivity. A secure enterprise password manager will ensure everyone has unique credentials for all their accounts and store them within an encrypted vault.
Data lakes contribute to the distribution of a company’s computing environments, particularly the cloud-based ones. Traditional network security and access control measures may not be enough to maintain a business’s cybersecurity posture in such circumstances. Switching to comprehensive modern solutions like Secure Access Service Edge (SASE) should become a priority.
There’s also the matter of governance and compliance. Companies hoping to benefit from data lakes must first establish data collection and identification standards. This will guarantee that stored information remains identifiable and accessible, preventing data lakes from turning into swamps.
In this context, addressing the proliferation of scam emails or even messages becomes crucial. There are ways to stop spam emails for personal use when they get annoying. Businesses, in the meantime, can significantly reduce the risk of phishing attempts and other email-based frauds by implementing sophisticated AI-driven filtering systems. These systems analyze patterns and markers of scam emails, effectively blocking them before they reach employees or compromise data integrity. It’s an essential step in safeguarding not only the data within the lakes but also the broader digital ecosystem of a company.
Conclusion
When set up and maintained properly, data lakes become the sandboxes AI can harness to unearth paradigm-shifting insights. Their synergy represents one of the most potent growth opportunities in tech yet. Capitalize on it now so you don’t fall behind very soon, but keep in mind the challenges that come with this technology.
